This is a drop-in, zero-config Actions harness for OpenAI's Codex agent. It uses GitHub Models for inference, so you don't need to set up any secrets - just copy-pasting the action into your repo should work as-is.
You may need to go into your settings and check the "allow Actions to open PRs" checkbox.
To use it, open an issue in your repo with [codex] in the issue name.
| img.img-explore { | |
| opacity: 0.75; | |
| } |
| import numpy as np | |
| import tensorflow as tf | |
| __author__ = "Sangwoong Yoon" | |
| def np_to_tfrecords(X, Y, file_path_prefix, verbose=True): | |
| """ | |
| Converts a Numpy array (or two Numpy arrays) into a tfrecord file. | |
| For supervised learning, feed training inputs to X and training labels to Y. | |
| For unsupervised learning, only feed training inputs to X, and feed None to Y. |
| """Script to illustrate usage of tf.estimator.Estimator in TF v1.3""" | |
| import tensorflow as tf | |
| from tensorflow.examples.tutorials.mnist import input_data as mnist_data | |
| from tensorflow.contrib import slim | |
| from tensorflow.contrib.learn import ModeKeys | |
| from tensorflow.contrib.learn import learn_runner | |
| # Show debugging output |
| """akmtdfgen: A Keras multithreaded dataframe generator. | |
| Works with Python 2.7 and Keras 2.x. | |
| For Python 3.x, need to fiddle with the threadsafe generator code. | |
| Test the generator_from_df() functions by running this file: | |
| python akmtdfgen.py |
| """Information Retrieval metrics | |
| Useful Resources: | |
| http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
| http://www.nii.ac.jp/TechReports/05-014E.pdf | |
| http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
| http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
| Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
| """ | |
| import numpy as np |
| <html> | |
| Hello World | |
| </html> |